Lesson 2



Intelligent Agents

Artificial Intelligence

AI strategies

Intelligent Agents

An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors.

A rational agent is one that does the "right thing". We use the term performance measure for the how the criteria that determine how successful an agent is.

A definition of artificial intelligence research: "The study and design of rational agents"

Agent function

The agent function maps from perception to actions: ff: PP → AA The agent program runs on the physical architecture to produce ff
Agent = Architecture + Program

Agent Interaction


Ex: The vaccum-cleaner agent

A vacuum-cleaner world with just two locations. Each location can be clean or dirty, and the agent can move left or right and can clean the square that it occupies:

Partial tabulation of a simple agent function for the vacuumcleaner world. The agent cleans the current square if it is dirty, otherwise it moves to the other square.

Performance measure

An objective criterion for success of an agent's behaviour (“cost”, “reward”, “utility”, “sustainable”…).

E.g., performance measure of a vacuum-cleaner?

Rational agents

Rational Agent: For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, based on the evidence provided by the percept sequence and whatever built-in knowledge the agent has.
Rationality is distinct from omniscience (all-knowing with infinite knowledge).
Agents can perform actions in order to modify future perceptions so as to obtain useful information (exploration).
An agent is autonomous if its behaviour is determined by its own perceptions & experience (with ability to learn and adapt) without depending solely on build- in knowledge.

Task environment

To design a rational agent, we must specify the task environment P.E.A.S.:

Example table:

Environment types I

Environment types II

Environment types III

Agent Types

Five basic types in order of increasing generality:

Reflex agent




We use rectangles to denote the current internal state of the agent’s decision process, and ovals to represent the background information used in the process.


Will work only if the correct decision can be made on the basis of only the current percept—that is, only if the environment is fully observable.

Model-based reflex agent


Model the state of the world by: modelling how the world changes how its actions change the world. This can work even with partial information.

Model-based, Goal-based agent


It keeps track of the world state as well as a set of goals it is trying to achieve, and chooses an action that will (eventually) lead to the achievement of its goals.
We need to predict: plan & search.

Model-based, utility-based agent


It uses a model of the world, along with a utility function that measures its preferences among states of the world. Then it chooses the action that leads to the best expected utility, where expected utility is computed by averaging over all possible outcome states, weighted by the probability of the outcome.

General learning agent


The “performance element” box represents what we have previously considered to be the whole agent program. Now, the “learning element” box gets to modify that program to improve its performance.

Summary